One of main reasons why survival for patients with primary liver cancer (PLC) has not improved in the past twenty years is the lack of approved novel therapies, with the exception of the limited success of sorafenib. Multiple phase III studies have failed to demonstrate any survival advantage for patients with liver cancer. We believe that this situation will change dramatically with the advent of immunotherapy and biologicals and by the implementation of precision cancer management strategy. Our optimism is supported by recent findings in the field of hepatocellular carcinoma (HCC). CCR is in a unique position to utilize its expertise in the area of immunotherapy and biologics to test these novel treatment options in patients with liver cancer. Molecular subgrouping and biomarker-guided molecularly-targeted therapies are promising approaches that will be implemented in our ongoing and future studies. One of the key elements of the NCI-LCP is to build an infrastructure which allows intramural investigators to expand on their research and not only collaborate with each other but also with extramural investigators. Our first effort is to build an NCI Intramural Research Program (IRP)-based collaborative translational science network of liver cancer clinical trial data, accompanying biospecimens and correlative laboratory data to determine why immunotherapy is effective in certain patients but not in others. We are building a national clinical network in liver cancer to: 1) define at the molecular level which group of PLC patients respond to immunotherapy and which do not and why some patients develop resistance/tumor relapse and 2) to determine whether any similarities or differences in response to immunotherapy are observed in HCC versus cholangiocarcinoma (CCA) patients. This program is based on the creation of a unique and robust national information commons comprised of comprehensive clinical and molecular data which can be utilized by the cancer research community for future studies of PLC. To accomplish these aims, the NCI IRP plans to serve as the leading and coordinating hub to leverage the ongoing immunotherapy-based clinical trials at major institutions treating PLC patients. We have partnered with clinical extramural collaborators across the U.S. to obtain biospecimens (biospy, blood, urine and fecal) and clinical data (medical chart data, treatment outcome and survival) from PLC patients in their ongoing clinical trials and/or standard of care patients treated with immune checkpoint inhibitors. We aim to collect biospecimens and clinical data from 1000 PLC patients over a 5-year period across study sites. Comprehensive correlative data will be performed at NCI and NCI Frederick National Laboratory, along with our collaborators, including molecular (DNA and RNA-Seq), single-cell sequencing, metabolomics, microbiome, immunophenotyping by CODEX 3D-multiplex imaging (Optical Microscopy and Image Analysis Laboratory) and SNP/variant analysis. Radiogenomics, pairing data mined from comprehensive multi-modality imaging with genomics, by artificial intelligence/machine learning, will also be used to screen and predict patient response to immunotherapy. Comprehensive statistical, machine learning and bioinformatics analysis will be used to integrate correlative data and clinical data to molecularly characterize and define predictive signatures of treatment responders and nonresponders to immunotherapy in all cases or in the stratified analyses. In concert with this effort, we have also initiated a pilot retrospective study of archival biopsy specimens collected before and after immunotherapy treatment from liver cancer patients from the NIH Clinical Center and Georgetown University. These samples will be assessed by DNA and RNA sequencing to identify mutations, transcripts and molecular signatures associated with patient outcome following immunotherapy treatment. Overall, we anticipate that these data will define specific subgroups of patients who are more likely to benefit from immune checkpoint inhibitor treatment. In addition, biomarkers and novel druggable targets may be identified to better determine or affect treatment response.